Remote sensing classification and extraction of winter wheat in Yangzhou based on machine learning algorithm
Satellite remote sensing technology is a commonly used monitoring and classification technology in crops at present.In order to achieve accurate classification and area extraction of regional winter wheat,Yangzhou City,Jiangsu Province had been taken as an example in this study.The Sentinel-2 satellite data and Shuttle Radar Topography Mission(SRTM)elevation data were used as data sources.Four machine learning algorithms,including classification and regression decision tree(CART),Gradient Boosted Decision Tree(GBDT),support vector machine(SVM)and Random forests(RF),were used to establish the classification model.At the same time,the MSI multispectral image of March 22,2021 in the study area was called and downloaded to extract parameters such as spectrum,texture and terrain features,and the winter wheat in the study area was classified and extracted,and the classification effect and accuracy of the four models were analyzed.The results showed that RF and GBDT classification methods had the best effect and the highest overall accuracy(OA),and both of the OA values were 0.967 and Kappa coefficient was 0.960.The OA of SVM classification method was the lowest(0.514),but the user accuracy(UA)was the highest(0.972).The method mentioned above could realize accurate classification and extraction of regional crops.
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